An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning With Crowdsourcing to Cloud Computing

Zhang, Qingchen; Yang, Laurence T.; Chen, Zhikui; Li, Peng; Bu, Fanyu

doi:10.1109/tii.2018.2791424

Cited by 112 publications

(45 citation statements)

References 16 publications

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“…Zhou et al have proposed a novel algorithm to increase the results of data forwarding in opportunistic mobile networks, forecast node social contact patterns beginning the temporal viewpoint. Zhang et al have proposed a dropout deep computation model by crowd sourcing in the direction of cloud for industrial IoT big data feature learning.…”

Section: Literature Reviewmentioning

confidence: 99%

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

Babu¹,

Venkataramanan²

2020

Int J Communication

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The vacant licensed spectrum can be employed by the secondary users in cognitive radio networks. Nevertheless, this process of identification is frequently agreed with shadowing, multipath fading and receiver uncertainty problems. The primary contributions of the spectrum sensing techniques adopted in this work mainly concentrate on energy detection for spectrum sensing as well as to recognize the optimal spectral estimation according to the multitaper spectral estimation. The algorithm proposed in this work is based on bivariate Lévy-stable bat algorithm (BLSBA), energy detector (ED), and with the help of K out of M fusion rule; it is analysed for the single user as well as cooperative multiple users. In the BLSBA algorithm, a modified search equation with more helpful information from the search experiences is brought-in to create an optimal energy solution and bivariate Lévy-stable random walk is associated with BLSBA to remove the trapping process into local optima. The energy detection is defined numerically from this optimal detection. At last, a multi-taper spectral estimator (MSE) is proposed to cognitive radio detection for a huge network. The simulation results in both cases are computed and checked with the help of a BLSBA optimizer. For an individual secondary user scenario, the advancement of MSE to the ED is broadly described. Experimental result indicates that the objective false alarm likelihood is minimized and the demanded signal-to-noise ratio is accomplished to the extent. INDEX TERMSbivariate Lévy-stable bat algorithm (BLSBA)cognitive radio networks (CRNs); energy detector (ED)multitaper spectral estimator (MSE)optimizationspectrum sensing

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Section: Literature Reviewmentioning

confidence: 99%

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

Babu¹,

Venkataramanan²

2020

Int J Communication

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“…In Fuzzy‐Based Routing Protocol (FBRP), which is the extension of REER, the delivery predictability worth is modeled. This is done using the energy value and the PD to define a routing path for packets . Some other algorithms like MP have considered buffer management as a metric to be relied on for routing.…”

Section: Related Workmentioning

confidence: 99%

“…The contact plan has been modeled using different fairness metrics in the studies of Arastouie and Sabaei and Zhang, Yang, Chen, Li, and Fanyu . The common method to decide which messages to be sent is meeting and visit (MV) method .…”

Section: Related Workmentioning

confidence: 99%

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Near‐optimal online routing in opportunistic networks

Arastouie

Sabaei

Bakhshi

2018

Int J Communication

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In opportunistic networks, nodes communicate intermittently based on storecarry-forward paradigm while exploiting node mobility. The challenge is to determine the ideal nodes to deliver the messages since there is no end-toend connectivity. The nodes might make this decision based on the data sensed from the network. This technique is not ideal in scenarios where the speed of changes in the network topology is greater than the speed at which the nodes can collect info on the network, which might, in turn, be restricted due to usage constraints and uncertainty of knowledge about future contacts. To tackle the problems raised by the non-deterministic environments, in this paper, a stochastic optimization model and corresponding algorithm are developed to find the optimal routes by considering the short and long-term impact of choices, ie, the next hop. Herein, we first propose a stochastic model to resolve the routing problem by identifying the shortest path. In the second step, we show that the optimal solution of the proposed model can be determined in polynomial time. An online algorithm is then proposed and analyzed. The algorithm is O(lognρ) competitive considering the number of nodes and their associated energy. This model can take advantage of the unexpected meets to make the routing more elastic in a short time of contact and with less of a burden on the buffer. The simulation results, against the prominent algorithms, demonstrate significant improvement of the proposed approach in delivery and average delay ratio.KEYWORDS opportunistic networks, stochastic optimization, online routing, competitive ratio | INTRODUCTIONOpportunistic networks (ONs) are the kind of networks where nodes are wirelessly and erratically connected using an ad hoc like structure. The communications between the nodes happen without a fixed communication infrastructure to support the mobility needs of the nodes. The challenges to be tackled by these networks include sporadic connection, long and variable delay/errors, and the fact that no end-to-end connectivity exists for these sparse and self-organized networks. When a node wants to transmit a message to another node with no direct connection, packets can be forwarded using relaying nodes to facilitate the packet delivery from the originating node to the final destination. Inherently, each node supports the store-carry-forward paradigm within the network. 1 Accidentally, moving nodes can facilitate intermittent connectivity cycle towards the ultimate destination. In some literature, the terms ON and delaytolerant network (DTN) are often used interchangeably. In our view, ONs are indeed a subset of DTNs (DTNs assume

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“…This will inevitably put forward higher requirements for automatic analysis and mining of big data. Deep learning [29][30][31] has been widely used to extract information from remote sensing images, and its accuracy has exceeded the accuracy of manual recognition. The great success of deep learning in the field of computer vision [32][33][34] provides an important opportunity for big data to extract information intelligence from remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

Sun

Zhang

2019

Wireless Communications and Mobile Computing

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Currently, big data is a new and hot object of research. In particular, the development of the Internet of things (IoT) results in a sharp increase in data. Enormous amounts of networking sensors are constantly collecting and transmitting data for storage and processing in the cloud including remote sensing data, environmental data, geographical data, etc. Road information extraction from remote sensing data is mainly researched in this paper. Roads are typical man-made objects. Extracting roads from remote sensing imagery has great significance in various applications such as GIS data updating, urban planning, navigation, and military. In this paper a multistage and multifeature method to extract roads and detect road intersections from high-resolution remotely sensed imagery based on tensor voting is presented. Firstly, the input remote sensing image is segmented into two groups including road candidate regions and nonroad regions using template matching; then we can obtain preliminary road map. Secondly, nonroad regions are removed by geometric characteristics of road (large area and long strip). Thirdly, tensor voting is used to overcome the broken roads and discontinuities caused by the different disturbing factors and then delete the nonroad areas that are mixed into the road areas due to mis-segmentation, improving the completeness of extracted roads. And then, all the road intersections are extracted by using tensor voting. The experiments are conducted on different remote sensing images to test the effectiveness of our method. The experimental results show that our method can get more complete and accurate extracted results than the state-of-the-art methods.

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An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning With Crowdsourcing to Cloud Computing

Cited by 112 publications

References 16 publications

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

Near‐optimal online routing in opportunistic networks

Roads and Intersections Extraction from High-Resolution Remote Sensing Imagery Based on Tensor Voting under Big Data Environment

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